保护隐私的大型语言模型用于匹配发现和跟踪纵向放射学报告的间隔变化
Tejas Sudharshan Mathai1, Boah Kim2, Oana M Stroie3
1Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224, Bethesda, MD, 20892-1182, USA. tejas.mathai@nih.gov.
Journal of imaging informatics in medicine
|April 11, 2025
概括
大型语言模型 (LLM) 可以在报告之间匹配放射学发现,并跟踪随时间的变化. TenyxChat-7B LLM 显示了中度至实质性的协议,提高了放射学报告的效率.
科学领域:
- 放射学中的人工智能
- 医疗报告的自然语言处理.
背景情况:
- 放射科医生手动比较以前和当前的成像报告,以评估变化.
- 大型语言模型 (LLM) 显示了识别报告发现的潜力,但需要验证来跟踪间隔变化.
研究的目的:
- 评估保护隐私的LLM对前期和后期放射学报告之间的发现相匹配的实用性.
- 评估LLM在追踪损伤大小间隔变化的能力.
主要方法:
- 一个两阶段的框架使用LLM匹配发现和预测间隔变化状态 (增加,减少,稳定).
- 对内部身体MRI和外部非对比胸部CT数据集的评估,与放射科医生同意使用科恩卡帕 (κ) 测量.
主要成果:
- TenyxChat-7B LLM在内部数据集上找到匹配的F1得分达到了85.4%.
- 对内部数据的间隔变化检测中等一致 (κ=0.46);对外部数据的实质一致 (κ=0.64).
- 在外部数据集上,LLM在匹配结果 (81.8%的F1得分) 和间隔变化 (77.4%) 中表现强.
结论:
- TenyxChat-7B LLM有效地匹配纵向放射学报告的发现,并跟踪间隔变化与中等到实质性的协议.
- 通过预先填写发现部分,LLM可以增强结构化报告,并改善放射科医生和转诊医生之间的沟通.
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